1. Field of the Invention
This invention relates to a radar device and specifically, to a radar device which estimates angles of a plurality of waves coming from objects maintaining high resolution.
2. Description of Related Art
A radar device basically requires large amounts of calculation for the processing of estimating angles of objects based on a plurality of waves coming from the objects maintaining high resolution, i.e., requires large amounts of calculation fox “high-resolution processing”. In addition, the higher resolution of angles is required, the more amount of signal processing is needed.
Among high-resolution processing methods, described below are relatively well-known processing methods in the order of decreasing resolution. The processing time, too, shortens in this order.
(1) ESPRIT> (2) MUSIC> (3) MINIMUM NORM METHOD> (4) LINEAR ESTIMATION METHOD> (5) CAPON METHOD> (6) DBF
The term “ESPRIT” is an abbreviation for Estimation of Signal Parameters via Rotational Invariance Techniques. “MUSIC” is an abbreviation for MUltiple SIgnal Classification, and “DBF” is an abbreviation for Digital Beam Forming.
If attention is paid to processing times, the processing time greatly differs particularly between (1) and (2), and between (3) and (4). This is because in the methods (1) and (2), the signal covariance matrix should be processed by the eigenvalue decomposition. Specifically, in the ESPRIT method (1), a plurality of eigenvalue decomposition processing is required. FIG. 1 is a flowchart of how to process the signals in the ESPRIT method. For simple description hereinafter, it is presumed that the radar device is equipped with a uniformly spaced linear-array antenna in which receiving antennas of a number of L are arranged on a straight line maintaining an equal distance.
At step S101, first, the number of incoming waves is estimated. Next, at step S102, a sub-array constituted by the receiving antennas of a number of N is taken out from the receiving antennas of the number of L while shifting the phase reference point, and a spatial smoothing method is applied to the signals received by a group of these sub-arrays (reference documents: S. U. Pillai and B. H. Kwon, Forward/Backward Spatial Smoothing Techniques for Coherent Signal Identification, IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-37, pp. 8-15, January, 1989). Next, at step S103, the Nsth-dimensional correlation matrix is processed by the eigenvalue decomposition, and signal subspace vectors are picked up at step S104.
In the case of the LS (least square) method, a regular matrix Ψ is calculated at step S105 relying on the method of least squares.
In the case of the TLS (total least square) method, on the other hand, the 2Nsth-dimensional expanded signal subspace matrix is processed by the eigenvalue decomposition at step S106. At step S107, a matrix of eigenvectors obtained at step S106 is formed. Next, at step S108, the regular matrix Ψ is calculated from the matrix of eigenvectors.
After the regular matrix Ψ is found by the LS method or the TLS method, eigenvalues of the Nsth-dimensional regular matrix Ψ is calculated at step S109, and angles are calculated at step S110.
Here, Ns is the number of incoming waves estimated by the well-known AIC (Akaike's Information Criterion) etc., and becomes Ns=N−1 at the time of a maximum processing load.
Among the processings based on the ESPRIT method, the step having the greatest processing load is an eigenvalue decomposition, and the load of processing increase with an increase in the dimension of a matrix. The frequency of performing the eigenvalue decomposition according to the ESPRIT method varies depending upon the method of processing the regular matrix Ψ appearing on the way of calculation, and is two times (steps S103 and S109) when the LS method is used and is three times (steps S103, S106 and S109) when the TLS method is used. According to the TLS method, in general, errors in the eigenvectors are minimized by using an expanded matrix and, therefore, a high degree of precision is attained through the frequency of performing the eigenvalue decomposition increases as compared to the LS method.
Prior to applying the ESPRIT method, it is normally necessary to estimate the number of incoming signals (or waves) by using eigenvalues of the covariance matrix (step S101—so, S101 requires a hidden eigenvalue decomposition, a OR-decomposition or the like). However, due to its poor precision it has been known that incorrect number often results. Further, the number of signals is estimated based on a trial-and-error-like calculation accounting for an increase in the processing time of the ESPRIT method.
In order to quicken the operation speed of the angle estimation processing, further, a method has been known for executing an object estimation processing by forming a pseudo-space smoothing covariance matrix and selecting from the formed matrix (e.g., patent document 1).
The problem that the invention is to solve is that a very long signal processing time is required by the ESPRIT method. Specifically, a car-mounted radar which must execute various processings inclusive of estimating the angles in very short periods of time is accompanied by a problem in that it is virtually difficult to estimate the angles relying on the conventional ESPRIT method.    [Patent document 1] JP-A-2009-210410